1. Technical Field
The invention relates generally to object identification and recognition. More particularly, one aspect of the invention is directed to monitoring and characterization of an object in an image, for example an animal or a person, using video analysis.
2. Background Art
Video analysis has developed over the past few decades to become an integral part of machine operations in manufacturing using machine automation. For example, video object recognition and pattern recognition has been used to orient and align various pieces of a product for machining and assembly in various manufacturing industries. One such use is in the manufacturing of semiconductor integrated circuits and microelectronic packaging. In this case, pattern recognition has made great inroads because the size of the work product is microscopic and orientation and alignment of the work product is thus far too tedious for a human being to do consistently and accurately over a large number of pieces.
In recent years, military has carried out research to use video to track moving targets such as tanks and vehicles, in the scene. Other positioning instruments such as global positioning system will be used to assist such tracking.
Another application for video analysis is monitoring animal activity in laboratory testing for the pharmaceutical and biological sciences. One particular area is monitoring animal behavior to determine the effects of various new drugs or gene changes on a particular type of animal. One such animal used in laboratory testing is the mouse.
Over the last two decades, major technological advances have enabled scientists to build a rich repository of mouse models. Model organisms are an important tool for understanding and dissecting human disease and biological process. Because mice and humans share many of the same fundamental biological and behavioral processes, this animal is one of the most significant laboratory models for human disease and studying biological processes in mammals. However, the adequate behavioral characterization (behavioral phenotypingxe2x80x94the impact of a genetic manipulation on visible characteristics of an organism) of genetically engineered mice is becoming a serious bottleneck in the development of animal models; an exponentially increasing number of genotypes are created, but the behavioral phenotyping is often at best rudimentary or is abandoned completely. This is because presently the phenotyping process is largely manual, time consuming, and insensitive to subtle phenotypes.
Video technologies for mouse behavior analysis have been introduced and several products are commercially available. However, these technologies are still primitive and the functionality of the products is far from adequate for the research purposes. There are presently two types of systems available for monitoring mouse behavior, those that identify individual behaviors and those that identify only the location of the mouse.
The most basic state-of-art mouse behavior analysis systems rely on traditional analog technologies that can only treat a mouse as an indivisible object and identify the mouse location. All the information about a mouse is packed as a point in the space and a lot of important information about mouse behavior is lost. The best these systems can do is to find the position of the mouse. Systems like San Diego Instruments"" Photobeam and AccuScan Instruments Inc.""s Digiscan Line of Animal Activity Monitoring, Columbus, Ohio uses simple and rudimentary photo-beams to detect and track the positions of mouse. These systems trackers have a very low spatial resolution, limiting their output to a rough measure of the animal""s activity. They cannot differentiate even such basic behaviors as locomotion and circling. Adding a time line for the locus of mouse point is all they can offer. Other animal location type systems used to monitor animal motion include those described in U.S. Pat. Nos. 3,100,473; 3,803,571; 3,974,798; 4,337,726; 4,574,734; and 5,816,256.
The other systems in the field are the systems that identify individual behavior using video. The existing video analysis systems (e.g. Noldus Observer/Ethovision, Sterling, VA; HVS Image, Hampton, UK; AccuScan Instruments Inc.""s VideoScan2000 System; and San Diego Instruments Poly-Track system, San Diego, Calif.) do not meet expectations either. Digitized images from video are used to capture the body of mouse and provide quantitative data about the position and movements of the animal and the pattern of these variables across time. They do not just treat the animal (e.g., mouse) as a point in the space. Instead, they handle it as a block of pixels. More information is preserved. However, they can only make use of a few simple features. For example, the mass center of the animal (e.g., mouse) is calculated and used as a means for tracking the animal (e.g., a mouse). As such, a lot of information that is critical to identify the animal""s behaviors such as different postures, positions of portions of the animal""s body such as limbs, is lost. These systems can only distinguish basic behaviors such as locomotion, and cannot automatically identify simple animal postures such as eating, rearing, and jumping, not to mention complex behaviors such as skilled reaching. Such behavior identification requires human intervention and input.
In addition, these systems are often developed for rats that remain relatively stationary in shape as they are in locomotion. However, other animals such as a mouse frequently stretch out, making their center of mass much less stable than a rat. As the center of gravity shifts rapidly and frequently, this falsely adds to measures such as distance traveled, making these systems highly inaccurate for mice. Further, the systems are devised to study white rats on a dark background and are not accurate for tracking other animals such as brown or black mice.
The most advanced systems are those offered by Noldus. The Noldus Observer system has a video camera, TV monitor, a high end VCR, and a PC system, all hooked together. The camera takes video footage of the mouse in a cage. This video is recorded on videotape, digitized, input into the PC system, and displayed on the computer monitor. Although the human observer can control the recorded video that is displayed, the human observer still needs to look at the animal on the screen, decide which behavior the animal is engaged in, and enter (by typing) the information into a mechanism provided by the system for storage and later analysis. While this system facilitates observation of behavior, it does not automate it, and is thus prone to human error and extremely labor intensive. The tasks of coding behavior throughout the day and building a profile of behavior for different types of animals and different strains of the same animal (e.g., different strains of mouse) is prohibitively time consuming with this equipment.
In general, the present invention is directed to systems and methods for finding patterns of behaviors and/or activities of an object using video. The invention includes a system with a video camera connected to a computer in which the computer is configured to automatically provide object identification, object motion tracking (for moving objects), object shape and posture classification, and behavior identification. Thus, the present invention is capable of automatically monitoring a video image to identify, track and classify the actions of various objects and their movements. The video image may be provided in real time from a camera and/or from a storage location. The invention is particularly useful for monitoring and classifying animal behavior for testing drugs and genetic mutations, but may be used in any of a number of surveillance or other applications.
In one embodiment the invention includes a system in which an analog video camera and a video record/playback device (e.g., VCR) are coupled to a video digitization/compression unit. The video camera may provide a video image containing an object to be identified. The video digitization/compression unit is coupled to a computer that is configured to automatically monitor the video image to identify, track and classify the actions of the object and its movements over time within a sequence of video session image frames. The digitization/compression unit may convert analog video and audio into, for example, MPEG or other formats. The computer may be, for example, a personal computer, using either a Windows platform or a Unix platform, or a Macintosh computer and compatible platform. The computer is loaded and configured with custom software programs (or equipped with firmware) using, for example, MATLAB or C/C++ programming language, so as to analyze the digitized video for object identification and segmentation, tracking, and/or behavior/activity characterization. This software may be stored in, for example, a program memory, which may include ROM, RAM, CD ROM and/or a hard drive, etc. In one variation of the invention the software (or firmware) includes a unique background subtraction method which is more simple, efficient, and accurate than those previously known.
In operation, the system receives incoming video images from either the video camera in real time or pre-recorded from the video record/playback unit. If the video is in analog format, then the information is converted from analog to digital format and may be compressed by the video digitization/compression unit. The digital video images are then provided to the computer where various processes are undertaken to identify and segment a predetermined object from the image. In a preferred embodiment the object is an object (e.g., a mouse) in motion with some movement from frame to frame in the video, and is in the foreground of the video images. In any case, the digital images may be processed to identify and segregate a desired (predetermined) object from the various frames of incoming video. This process may be achieved using, for example, background subtraction, mixture modeling, robust estimation, and/or other processes.
The shape and location of the desired object is then tracked from one frame or scene to another frame or scene of video images. Next, the changes in the shapes, locations, and/or postures of the object of interest may be identified, their features extracted, and classified into meaningful categories, for example, vertical positioned side view, horizontal positioned side view, vertical positioned front view, horizontal positioned front view, moving left to right, etc. Then, the shape, location, and posture categories may be used to characterize the object""s activity into one of a number of pre-defined behaviors. For example, if the object is an animal, some pre-defined normal behaviors may include sleeping, eating, drinking, walking, running, etc., and pre-defined abnormal behavior may include spinning vertical, jumping in the same spot, etc. The pre-defined behaviors may be stored in a database in the data memory. The behavior may be characterized using, for example, approaches such as rule-based label analysis, token parsing procedure, and/or Hidden Markov Modeling (HMM). Further, the system may be constructed to characterize the object behavior as new behavior and particular temporal rhythm.
In another preferred embodiment directed toward video analysis of animated objects such as animals, the system operates as follows. As a preliminary matter, normal postures and behaviors of the animals are defined and may be entered into a Normal Postures and Behaviors database. In analyzing in a first instant, incoming video images are received. The system determines if the video images are in analog or digital format and input into a computer. If the video images are in analog format they are digitized and may be compressed, using, for example, an MPEG digitizer/compression unit. Otherwise, the digital video image may be input directly to the computer. Next, a background may be generated or updated from the digital video images and foreground objects detected. Next, the foreground objects features are extracted. Then, the foreground object shape is classified into various categories, for example, standing, sitting, etc. Next, the foreground object posture is compared to the various predefined postures stored in the database, and then identified as a particular posture or a new (unidentified) posture. Then, various groups of postures are concatenated into a series to make up a foreground object behavior and then compared against the sequence of postures, stored in for example a database in memory, that make up known normal or abnormal behaviors of the animal. The abnormal behaviors are then identified in terms of known abnormal behavior, new behavior and/or daily rhythm.
In one variation of the invention, object detection is performed through a unique method of background subtraction. First, the incoming digital video signal is split into individual images (frames) in real-time. Then, the system determines if the background image derived from prior incoming video needs to be updated due to changes in the background image or a background image needs to be developed because there was no background image was previously developed. If the background image needs to be generated, then a number of frames of video image, for example 20, will be grouped into a sample of images. Then, the system creates a standard deviation map of the sample of images. Next, the process removes a bounding box area in each frame or image where the variation within the group of images is above a predetermined threshold (i.e., where the object of interest or moving objects are located). Then, the various images within the sample less the bounding box area are averaged. Final background is obtained by averaging 5-10 samples. This completes the background generation process. However, often the background image does not remain constant for a great length of time due to various reasons. Thus, the background needs to be recalculated periodically as above or it can be recalculated by keeping track of the difference image and note any sudden changes. The newly generated background image is next subtracted from the current video image(s) to obtain foreground areas that may include the object of interest.
Next, the object identification/detection process is performed. First, regions of interest (ROI) are obtained by identifying areas where the intensity difference generated from the subtraction is greater than a predetermined threshold, which constitute potential foreground object(s) being sought. Classification of these foreground regions of interest will be performed using the sizes of the ROIs, distances among these ROIs, threshold of intensity, and connectedness, to thereby identify the foreground objects. Next, the foreground object identification/detection process may be refined by adaptively learning histograms of foreground ROIs and using edge detection to more accurately identify the desired object(s). Finally, the information identifying the desired foreground object is output. The process may then continue with the tracking and/or behavior characterization step(s).
The previous embodiments are particularly applicable to the study and analysis of mice used in genetic and drug experimentation. One variation of the present invention is directed particularly to automatically determining the behavioral characteristics of a mouse in a home cage. The need for sensitive detection of novel phenotypes of genetically manipulated or drug-administered mice demands automation of analyses. Behavioral phenotypes are often best detected when mice are unconstrained by experimenter manipulation. Thus, automation of analysis of behavior in a known environment, for example a home cage, would be a powerful tool for detecting phenotypes resulting from gene manipulations or drug administrations. Automation of analysis would allow quantification of all behaviors as they vary across the daily cycle of activity. Because gene defects causing developmental disorders in humans usually result in changes in the daily rhythm of behavior, analysis of organized patterns of behavior across the day may also be effective in detecting phenotypes in transgenic and targeted mutant mice. The automated system may also be able to detect behaviors that do not normally occur and present the investigator with video clips of such behavior without the investigator having to view an entire day or long period of mouse activity to manually identify the desired behavior.
The systematically developed definition of mouse behavior that is detectable by the automated analysis according to the present invention makes precise and quantitative analysis of the entire mouse behavior repertoire possible for the first time. The various computer algorithms included in the invention for automating behavior analysis based on the behavior definitions ensure accurate and efficient identification of mouse behaviors. In addition, the digital video analysis techniques of the present invention improves analysis of behavior by leading to: (1) decreased variance due to non-disturbed observation of the animal; (2) increased experiment sensitivity due to the greater number of behaviors sampled over a much longer time span than ever before possible; and (3) the potential to be applied to all common normative behavior patterns, capability to assess subtle behavioral states, and detection of changes of behavior patterns in addition to individual behaviors.
Development activities have been completed to validate various scientific definitions of mouse behaviors and to create novel digital video processing algorithms for mouse tracking and behavior recognition, which are embodied in a software and hardware system according to the present invention. An automated method for analysis of mouse behavior from digitized 24 hour video has been achieved using the present invention and its digital video analysis method for object identification and segmentation, tracking, and classification. Several different methods and their algorithms, including Background Subtraction, Probabilistic approach with Expectation-Maximization, and Robust Estimation to find parameter values by best fitting a set of data measurements and results proved successful. The entire behavioral repertoire of individual mice in their home cage was categorized using successive iterations by manual videotape analysis. These manually defined behavior categories constituted the basis of automatic classification. Classification criteria (based on features extracted from the foreground object such as shape, position, movement) were derived and fitted into a decision tree (DT) classification algorithm. The decision tree could classify almost 500 sample features into 5 different postures classes with an accuracy over 93%. A simple HMM system has been built using dynamic programming and has been used to classify the classified postures identified by the DT and yields an almost perfect mapping from input posture to output behaviors in mouse behavior sequences.
The invention may identify some abnormal behavior by using video image information (for example, stored in memory) of known abnormal animals to build a video profile for that behavior. For example, video image of vertical spinning while hanging from the cage top was stored to memory and used to automatically identify such activity in mice. Further, abnormalities may also result from an increase in any particular type of normal behavior. Detection of such new abnormal behaviors may be achieved by the present invention detecting, for example, segments of behavior that do not fit the standard profile. The standard profile may be developed for a particular strain of mouse whereas detection of abnormal amounts of a normal behavior can be detected by comparison to the statistical properties of the standard profile.
Thus, the automated analysis of the present invention may be used to build profiles of the behaviors, their amount, duration, and daily cycle for each animal, for example each commonly used strain of mice. A plurality of such profiles may be stored in, for example, a database in a data memory of the computer. One or more of these profile may then be compared to a mouse in question and difference from the profile expressed quantitatively.
The techniques developed with the present invention for automation of the categorization and quantification of all home-cage mouse behaviors throughout the daily cycle is a powerful tool for detecting phenotypic effects of gene manipulations in mice. As previously discussed, this technology is extendable to other behavior studies of animals and humans, as well as surveillance purposes. As will be described in detail below, the present invention provides automated systems and methods for automated accurate identification, tracking and behavior categorization of an object whose image is captured with video.